Kavli Affiliate: Feng Wang
| First 5 Authors: Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang
| Summary:
The study of structure-spectrum relationships is essential for spectral
interpretation, impacting structural elucidation and material design.
Predicting spectra from molecular structures is challenging due to their
complex relationships. Herein, we introduce NMRNet, a deep learning framework
using the SE(3) Transformer for atomic environment modeling, following a
pre-training and fine-tuning paradigm. To support the evaluation of NMR
chemical shift prediction models, we have established a comprehensive benchmark
based on previous research and databases, covering diverse chemical systems.
Applying NMRNet to these benchmark datasets, we achieve state-of-the-art
performance in both liquid-state and solid-state NMR datasets, demonstrating
its robustness and practical utility in real-world scenarios. This marks the
first integration of solid and liquid state NMR within a unified model
architecture, highlighting the need for domainspecific handling of different
atomic environments. Our work sets a new standard for NMR prediction, advancing
deep learning applications in analytical and structural chemistry.
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